docstr: Load existing BigDL model or checkpoint
  
  :param checkpoint: Path to the existing model or checkpoint.
  :param optimizer: BigDL optimizer.
  :param loss: BigDL criterion.
  :param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
         The param converts the data in feature column to a Tensor or to a Sample directly.
         It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F,
         Tensor[T]]
  
         If a List of Int is set as feature_preprocessing, it can only handle the case that
         feature column contains the following data types:
         Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The
         feature data are converted to Tensors with the specified sizes before
         sending to the model. Internally, a SeqToTensor is generated according to the
         size, and used as the feature_preprocessing.
  
         Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]]
         that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are
         provided in package zoo.feature. Multiple Preprocessing can be combined as a
         ChainedPreprocessing.
  
         The feature_preprocessing will also be copied to the generated NNModel and applied
         to feature column during transform.
  :param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
         similar to feature_preprocessing, but applies to Label data.
  :param model_dir: The path to save model. During the training, if checkpoint_trigger is
         defined and triggered, the model will be saved to model_dir.
  :param is_checkpoint: Whether the path is a checkpoint or a saved BigDL model.
         Default: False.
  :return: The loaded estimator object.
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code: def load(
      self,
      checkpoint,
      optimizer=None,
      loss=None,
      feature_preprocessing=None,
      label_preprocessing=None,
      model_dir=None,
      is_checkpoint=False,
  ):
      """
      Load existing BigDL model or checkpoint
  
      :param checkpoint: Path to the existing model or checkpoint.
      :param optimizer: BigDL optimizer.
      :param loss: BigDL criterion.
      :param feature_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
             The param converts the data in feature column to a Tensor or to a Sample directly.
             It expects a List of Int as the size of the converted Tensor, or a Preprocessing[F,
             Tensor[T]]
  
             If a List of Int is set as feature_preprocessing, it can only handle the case that
             feature column contains the following data types:
             Float, Double, Int, Array[Float], Array[Double], Array[Int] and MLlib Vector. The
             feature data are converted to Tensors with the specified sizes before
             sending to the model. Internally, a SeqToTensor is generated according to the
             size, and used as the feature_preprocessing.
  
             Alternatively, user can set feature_preprocessing as Preprocessing[F, Tensor[T]]
             that transforms the feature data to a Tensor[T]. Some pre-defined Preprocessing are
             provided in package zoo.feature. Multiple Preprocessing can be combined as a
             ChainedPreprocessing.
  
             The feature_preprocessing will also be copied to the generated NNModel and applied
             to feature column during transform.
      :param label_preprocessing: Used when data in `fit` and `predict` is a Spark DataFrame.
             similar to feature_preprocessing, but applies to Label data.
      :param model_dir: The path to save model. During the training, if checkpoint_trigger is
             defined and triggered, the model will be saved to model_dir.
      :param is_checkpoint: Whether the path is a checkpoint or a saved BigDL model.
             Default: False.
      :return: The loaded estimator object.
      """
      if loss is not None:
          self.loss = loss
      if optimizer is not None:
          self.optimizer = optimizer
      if feature_preprocessing is not None:
          self.feature_preprocessing = feature_preprocessing
      if label_preprocessing is not None:
          self.label_preprocessing = label_preprocessing
      if model_dir is not None:
          self.model_dir = model_dir
  
      if is_checkpoint:
          self.load_orca_checkpoint(checkpoint)
      else:
          from zoo.pipeline.api.net import Net
  
          self.model = Net.load_bigdl(
              checkpoint + ".bigdl", checkpoint + ".bin"
          )
  
          self.nn_estimator = NNEstimator(
              self.model,
              self.loss,
              self.feature_preprocessing,
              self.label_preprocessing,
          )
          if self.optimizer is None:
              from bigdl.optim.optimizer import SGD
  
              self.optimizer = SGD()
          self.nn_estimator.setOptimMethod(self.optimizer)
          self.estimator = SparkEstimator(
              self.model, self.optimizer, self.model_dir
          )
          self.nn_model = NNModel(
              self.model,
              feature_preprocessing=self.feature_preprocessing,
          )
      return self
  
imports: from zoo.orca.learn.metrics import Metric
  from zoo.orca.learn.utils import bigdl_metric_results_to_dict
  from zoo.pipeline.nnframes import NNEstimator, NNModel
  from zoo.pipeline.estimator import Estimator
  from zoo.orca.learn.spark_estimator import Estimator
  from zoo.orca.data import SparkXShards
  from bigdl.optim.optimizer import MaxEpoch
  from zoo.feature.common import FeatureSet
  from zoo.orca.learn.metrics import Accuracy
  from pyspark.sql.dataframe import DataFrame
label_desc: Use image node_id 7 for calling the BigDLEstimator obj's underlying member method code with example usage: obj.load(checkpoint, optimizer, loss, feature_preprocessing, label_preprocessing, model_dir, is_checkpoint) and returns: self